[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118669-en":3,"doc-seo-118669-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118669,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Systematic Review of Machine Learning applications in Marine Engineering - Research Review","The document presents a systematic review of machine learning applications in marine engineering, compiling 91 papers selected from the top 25% of journals in the Engineering, Marine subcategory. The analysis identifies tree-based algorithms as the most widely used methods (46 papers), with multilayer perceptrons and other feed-forward neural networks (24 papers) and support vector machines (21 papers) as leading single algorithms. It also maps common data types toward numerical tabular and time-series numerical datasets, and highlights vessel fuel optimization as the most covered topic (11 papers).","Systematic Review of Machine Learning applications in Marine Engineering  \nSustavnipregled primjene strojnogučenja u brodostrojarstvu  \nIgor Poljak  \nUniversity of Zadar Department of Maritime Sciences Zadar, Croatia  \nE-mail: [ipoljak1@unizd.hr](ipoljak1@unizd.hr)  \nSandi Baressi Šegota* Juraj Dobrila University of Pula Faculty of Informatics  \nPula, Croatia  \nE-mail: [sandi.baressi.segota@unipu.hr](sandi.baressi.segota@unipu.hr)  \nVedran Mrzljak University of Rijeka Faculty of Engineering Rijeka, Croatia  \n[E-mail: vedran.mrzljak@riteh.uniri.hr](E-mail: vedran.mrzljak@riteh.uniri.hr)  \nNikola Anđelić  \nUniversity of Rijeka Faculty of Engineering Rijeka, Croatia  \n[E-mail: nikola.andjelic@riteh.uniri.hr](E-mail: nikola.andjelic@riteh.uniri.hr)  \nAbstract  \nThe application of machine learning techniques isan ever-growing trend in engineering fields-with marine engineering being far from an exception. The authors present a total of 91 papers selected from the top 25% journals in the “Engineering, Marine”subcategory – selecting papers that applied machine learning algorithms to novel problems in marine engineering. The results show that most researchers prefer the utilization of tree-based algorithms, which were most commonly used across papers (46 papers accounting for different tree-based methodologies), with the single algorithms that were most popular being the multilayer perceptron (and other feed-forward artificial neural networks of the same shape), used in 24 papers and support vector machines (used in 21 papers). The analysis of goals and applied methods indicates that the most common analysis in marine engineering is performed on numerical, tabular, data–followed by the time-series numerical data. The most covered topic present in the research is optimizing the fuel use of vessels (11 papers), followed by the application of machine learning in the modeling of vessel dynamicsand the preliminary vessel design (10 papers each). Based on the existing trends, the field of machine learning application for marine engineering will only continue to grow.  \nSažetak  \nPrimjena metoda strojnog učenja sve je izraženiji trend u inženjerskim područjima, pri čemu ni brodostrojarstvo nije iznimka. Autori predstavljaju ukupno 91 rad odabran iznajboljih 25% časopisa u potkategoriji „strojarstvo, pomorstvo“ odabirući radove koji su primijenili algoritme strojnog učenja na nove probleme u brodostrojarstvu. Rezultatipokazuju da većina istraživača preferira uporabu algoritama temeljenih na stablima, koji su najčešće korišteni u radovima (46 radova koji obrađuju različite metodologije temeljene na stablima), dok su pojedinačno najpopularniji algoritmi višeslojnog perceptrona (i druge umjetne neuronske mreže istog tipa), korišteni u 24 rada, te strojevipotpornih vektora (korišteni u 21 radu). Analiza ciljeva i primijenjenih metoda pokazujedasenajčešća analiza u brodostrojarstvu provodina numeričkim, tabličnim podacima, apotom nanumeričkim vremenskim nizovima. Najčešćeobrađivana tema uistraživanjima jest optimizacija brodskepotrošnjegoriva (11 radova), a slijedi primjenastrojnog učenjau modeliranju dinamike plovila i preliminarnom projektiranju plovila (po 10 radova). Na temelju postojećih trendova područje primjene strojnog učenja u brodostrojarstvunastavit će rasti.  \nDOI 10. 17818/NM/2025/3 .3 UDK 004.896:629.5 Review / Pregledni rad  \nPaper received / Rukopis primljen: 4. 3. 2025. Paper accepted / Rukopis prihvaćen: 2. 10. 2025.  \nThis work is licensed under a Creative Commons Attribution 4.0 International License.  \nKEYWORDS machine learning marine engineering systematic review  \nKLJUČNERIJEČIstrojno učenjebrodostrojarstvosustavnipregled  \n1. INTRODUCTION / Uvod  \nAn ever-growing trend in research is the application of artificial intelligence-related techniques in various fields [1], including engineering [2]. One of the most commonly applied areas of artificial intelligence is machine learning-based modeling. In machine learning, the mode","cbCaie5zrJp4SSEY","https://ap.wps.com/l/cbCaie5zrJp4SSEY","pdf",1268585,1,12,"English","en",105,"# Abstract\n# Introduction / Uvod\n## Growth of machine learning publications in marine engineering","[{\"question\":\"How many papers are included in the systematic review and how were they selected?\",\"answer\":\"The review includes 91 papers selected from journals in the Engineering, Marine subcategory, restricted to the top 25%.\"},{\"question\":\"Which machine learning algorithm families are most commonly used in marine engineering papers?\",\"answer\":\"Tree-based algorithms are most common (46 papers). Multilayer perceptrons and other feed-forward neural networks (24 papers) and support vector machines (21 papers) follow.\"},{\"question\":\"What marine engineering problem and data types appear most frequently in the reviewed studies?\",\"answer\":\"Fuel optimization of vessels is the most covered topic (11 papers). Analyses most often use numerical tabular data, followed by time-series numerical data.\"}]","Systematic Review of Machine Learning applications in Marine Engineering - Research Review | PDF",1785684812,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"systematic-review-of-machine-learning-applications-in-marine-engineering-research-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/systematic-review-of-machine-learning-applications-in-marine-engineering-research-review/118669/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How many papers are included in the systematic review and how were they selected?","Question",{"text":75,"@type":76},"The review includes 91 papers selected from journals in the Engineering, Marine subcategory, restricted to the top 25%.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithm families are most commonly used in marine engineering papers?",{"text":80,"@type":76},"Tree-based algorithms are most common (46 papers). Multilayer perceptrons and other feed-forward neural networks (24 papers) and support vector machines (21 papers) follow.",{"name":82,"@type":73,"acceptedAnswer":83},"What marine engineering problem and data types appear most frequently in the reviewed studies?",{"text":84,"@type":76},"Fuel optimization of vessels is the most covered topic (11 papers). 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